Real-Time Wireless Channel Prediction Based on Online Federated Learning

For current and future wireless communication networks, accurate Channel State Information (CSI) is pivotal for delivering high-quality services for countless devices. Traditional Channel Estimation (CE) methods grapple with channel aging, leading to inaccuracies. In contrast, we obtain the precise CSI by predicting channels based on neural networks, which have outstanding performance in feature extraction, data generation and prediction. Centralized Learning (CL) methods impose high demands on computational resources, storage and communication overhead. Moreover, it poses a risk to user data privacy. Consequently, we propose a Federated Learning-based Long Short Term Memory (FL-LSTM) Neural Network for real-time CSI prediction. We validate our model on the simulation dataset and measurement datasets. Simulation results show that our approach can predict the CSI data effectively, and its prediction performance approximates to Centralized Learning-based Long Short Term Memory (CL-LSTM) Neural Network. In addition, the global model is capable of generalization, which can be transferred to a new base station (BS) to accelerate the convergence of the local model and reduce the prediction error.